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Using Reward-weighted Regression for Reinforcement Learning of Task Space Control

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Peters, J. (2007). Using Reward-weighted Regression for Reinforcement Learning of Task Space Control. In IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL 2007) (pp. 262-267). Los Alamitos, CA, USA: IEEE Computer Society.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CE29-8
Abstract
Many robot control problems of practical importance, including task or operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan amp; Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots.